255 lines
8.2 KiB
Python
255 lines
8.2 KiB
Python
import numpy as np
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from .float import *
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from .layer_base import *
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from .initialization import *
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class Bias(Layer):
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# TODO: support axes other than -1 and shapes other than 1D.
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serialized = {
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'b': 'biases',
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}
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def __init__(self, init=init_zeros, reg_b=None):
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super().__init__()
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self.biases = self._new_weights('biases', init=init, regularizer=reg_b)
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def make_shape(self, parent):
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shape = parent.output_shape
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self.input_shape = shape
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self.output_shape = shape
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self.biases.shape = (shape[-1],)
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def forward(self, X):
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return X + self.biases.f
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def backward(self, dY):
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self.biases.g += dY.sum(0)
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return dY
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class Dense(Layer):
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serialized = {
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'W': 'coeffs',
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'b': 'biases',
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}
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def __init__(self, dim, init=init_he_uniform, reg_w=None, reg_b=None):
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super().__init__()
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self.dim = int(dim)
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self.output_shape = (dim,)
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self.coeffs = self._new_weights('coeffs', init=init, regularizer=reg_w)
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self.biases = self._new_weights('biases', init=init_zeros, regularizer=reg_b)
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def make_shape(self, parent):
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shape = parent.output_shape
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self.input_shape = shape
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assert len(shape) == 1, shape
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self.coeffs.shape = (shape[0], self.dim)
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self.biases.shape = (1, self.dim)
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def forward(self, X):
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self.X = X
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return X @ self.coeffs.f + self.biases.f
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def backward(self, dY):
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self.coeffs.g += self.X.T @ dY
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self.biases.g += dY.sum(0, keepdims=True)
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return dY @ self.coeffs.f.T
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# more
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class Conv1Dper(Layer):
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# periodic (circular) convolution.
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# currently only supports one channel I/O.
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# some notes:
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# we could use FFTs for larger convolutions.
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# i think storing the coefficients backwards would
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# eliminate reversal in the critical code.
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serialize = {
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'W': 'coeffs',
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}
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def __init__(self, kernel_size, pos=None,
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init=init_glorot_uniform, reg_w=None):
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super().__init__()
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self.kernel_size = int(kernel_size)
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self.coeffs = self._new_weights('coeffs', init=init, regularizer=reg_w)
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if pos is None:
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self.wrap0 = (self.kernel_size - 0) // 2
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self.wrap1 = (self.kernel_size - 1) // 2
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elif pos == 'alt':
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self.wrap0 = (self.kernel_size - 1) // 2
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self.wrap1 = (self.kernel_size - 0) // 2
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elif pos == 'left':
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self.wrap0 = 0
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self.wrap1 = self.kernel_size - 1
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elif pos == 'right':
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self.wrap0 = self.kernel_size - 1
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self.wrap1 = 0
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else:
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raise Exception("pos parameter not understood: {}".format(pos))
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def make_shape(self, parent):
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shape = parent.output_shape
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self.input_shape = shape
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assert len(shape) == 1, shape
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self.output_shape = shape
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self.coeffs.shape = (1, self.kernel_size)
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def forward(self, X):
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if self.wrap0 == 0:
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Xper = np.hstack((X,X[:,:self.wrap1]))
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elif self.wrap1 == 0:
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Xper = np.hstack((X[:,-self.wrap0:],X))
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else:
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Xper = np.hstack((X[:,-self.wrap0:],X,X[:,:self.wrap1]))
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self.cols = rolling_batch(Xper, self.kernel_size)
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convolved = (self.cols * self.coeffs.f[:,::-1]).sum(2)
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return convolved
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def backward(self, dY):
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self.coeffs.g += (dY[:,:,None] * self.cols).sum(0)[:,::-1].sum(0, keepdims=True)
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return (dY[:,:,None] * self.coeffs.f[:,::-1]).sum(2)
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class LayerNorm(Layer):
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# paper: https://arxiv.org/abs/1607.06450
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# note: nonparametric when affine == False
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def __init__(self, eps=1e-5, affine=True):
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super().__init__()
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self.eps = _f(eps)
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self.affine = bool(affine)
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if self.affine:
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self.gamma = self._new_weights('gamma', init=init_ones)
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self.beta = self._new_weights('beta', init=init_zeros)
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self.serialized = {
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'gamma': 'gamma',
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'beta': 'beta',
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}
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def make_shape(self, parent):
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shape = parent.output_shape
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self.input_shape = shape
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self.output_shape = shape
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assert len(shape) == 1, shape
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if self.affine:
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self.gamma.shape = (shape[0],)
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self.beta.shape = (shape[0],)
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def forward(self, X):
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self.mean = X.mean(0)
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self.center = X - self.mean
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self.var = self.center.var(0) + self.eps
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self.std = np.sqrt(self.var)
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self.Xnorm = self.center / self.std
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if self.affine:
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return self.gamma.f * self.Xnorm + self.beta.f
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return self.Xnorm
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def backward(self, dY):
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length = dY.shape[0]
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if self.affine:
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dXnorm = dY * self.gamma.f
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self.gamma.g += (dY * self.Xnorm).sum(0)
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self.beta.g += dY.sum(0)
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else:
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dXnorm = dY
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dstd = (dXnorm * self.center).sum(0) / -self.var
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dcenter = dXnorm / self.std + dstd / self.std * self.center / length
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dmean = -dcenter.sum(0)
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dX = dcenter + dmean / length
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return dX
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class Denses(Layer): # TODO: rename?
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# acts as a separate Dense for each row or column. only for 2D arrays.
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serialized = {
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'W': 'coeffs',
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'b': 'biases',
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}
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def __init__(self, dim, init=init_he_uniform, reg_w=None, reg_b=None, axis=-1):
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super().__init__()
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self.dim = int(dim)
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self.weight_init = init
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self.axis = int(axis)
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self.coeffs = self._new_weights('coeffs', init=init, regularizer=reg_w)
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self.biases = self._new_weights('biases', init=init_zeros, regularizer=reg_b)
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def make_shape(self, parent):
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shape = parent.output_shape
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self.input_shape = shape
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assert len(shape) == 2, shape
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assert -len(shape) <= self.axis < len(shape)
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self.axis = self.axis % len(shape)
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self.output_shape = list(shape)
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self.output_shape[self.axis] = self.dim
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self.output_shape = tuple(self.output_shape)
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in_rows = self.input_shape[0]
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in_cols = self.input_shape[1]
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out_rows = self.output_shape[0]
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out_cols = self.output_shape[1]
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self.coeffs.shape = (in_rows, in_cols, self.dim)
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self.biases.shape = (1, out_rows, out_cols)
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def forward(self, X):
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self.X = X
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if self.axis == 0:
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return np.einsum('ixj,xjk->ikj', X, self.coeffs.f) + self.biases.f
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elif self.axis == 1:
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return np.einsum('ijx,jxk->ijk', X, self.coeffs.f) + self.biases.f
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def backward(self, dY):
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self.biases.g += dY.sum(0, keepdims=True)
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if self.axis == 0:
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self.coeffs.g += np.einsum('ixj,ikj->xjk', self.X, dY)
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return np.einsum('ikj,xjk->ixj', dY, self.coeffs.f)
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elif self.axis == 1:
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self.coeffs.g += np.einsum('ijx,ijk->jxk', self.X, dY)
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return np.einsum('ijk,jxk->ijx', dY, self.coeffs.f)
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class CosineDense(Dense):
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# paper: https://arxiv.org/abs/1702.05870
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# another implementation: https://github.com/farizrahman4u/keras-contrib/pull/36
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# the paper doesn't mention bias,
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# so we treat bias as an additional weight with a constant input of 1.
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# this is correct in Dense layers, so i hope it's correct here too.
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eps = 1e-4
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def forward(self, X):
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self.X = X
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self.X_norm = np.sqrt(np.square(X).sum(-1, keepdims=True) \
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+ 1 + self.eps)
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self.W_norm = np.sqrt(np.square(self.coeffs.f).sum(0, keepdims=True) \
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+ np.square(self.biases.f) + self.eps)
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self.dot = X @ self.coeffs.f + self.biases.f
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Y = self.dot / (self.X_norm * self.W_norm)
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return Y
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def backward(self, dY):
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ddot = dY / self.X_norm / self.W_norm
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dX_norm = -(dY * self.dot / self.W_norm).sum(-1, keepdims=True) / self.X_norm**2
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dW_norm = -(dY * self.dot / self.X_norm).sum( 0, keepdims=True) / self.W_norm**2
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self.coeffs.g += self.X.T @ ddot \
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+ dW_norm / self.W_norm * self.coeffs.f
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self.biases.g += ddot.sum(0, keepdims=True) \
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+ dW_norm / self.W_norm * self.biases.f
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dX = ddot @ self.coeffs.f.T + dX_norm / self.X_norm * self.X
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return dX
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